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Bridging the gap between measurements and modelling: a cardiovascular functional avatar
Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0003-1942-7699
Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
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2017 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, article id 6214Article in journal (Refereed) Published
Abstract [en]

Lumped parameter models of the cardiovascular system have the potential to assist researchers and clinicians to better understand cardiovascular function. The value of such models increases when they are subject specific. However, most approaches to personalize lumped parameter models have thus far required invasive measurements or fall short of being subject specific due to a lack of the necessary clinical data. Here, we propose an approach to personalize parameters in a model of the heart and the systemic circulation using exclusively non-invasive measurements. The personalized model is created using flow data from four-dimensional magnetic resonance imaging and cuff pressure measurements in the brachial artery. We term this personalized model the cardiovascular avatar. In our proof-of-concept study, we evaluated the capability of the avatar to reproduce pressures and flows in a group of eight healthy subjects. Both quantitatively and qualitatively, the model-based results agreed well with the pressure and flow measurements obtained in vivo for each subject. This non-invasive and personalized approach can synthesize medical data into clinically relevant indicators of cardiovascular function, and estimate hemodynamic variables that cannot be assessed directly from clinical measurements.

Place, publisher, year, edition, pages
Nature Publishing Group, 2017. Vol. 7, article id 6214
National Category
Biomedical Laboratory Science/Technology
Identifiers
URN: urn:nbn:se:liu:diva-140069DOI: 10.1038/s41598-017-06339-0ISI: 000406260100018PubMedID: 28740184Scopus ID: 2-s2.0-85025821468OAI: oai:DiVA.org:liu-140069DiVA, id: diva2:1136565
Note

Funding Agencies|European Research Council [310612]; Swedish Research Council [2014-6191]

Available from: 2017-08-28 Created: 2017-08-28 Last updated: 2017-09-05Bibliographically approved

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Casas Garcia, BelénLantz, JonasViola, FredericaCedersund, GunnarBolger, Ann F.Carlhäll, CarljohanKarlsson, MattsEbbers, Tino
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Division of Cardiovascular MedicineFaculty of Medicine and Health SciencesCenter for Medical Image Science and Visualization (CMIV)Division of Biomedical EngineeringFaculty of Science & EngineeringDepartment of Clinical Physiology in LinköpingApplied Thermodynamics and Fluid Mechanics
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